Improve the signal to noise ratio and installation convenience of the inductive coil for wire rope nondestructive testing

Yan, Xiaolan; Zhang, Donglai; Zhao, Fei

doi:10.1016/j.ndteint.2017.09.005

Cited by 42 publications

(13 citation statements)

References 13 publications

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“…Two parallel connected flexible flat coils fed with a biased pulse current were deployed by the sensor as the electromagnet for cable magnetization. Yan et al [31] put forward a kind of iron core as coil winding skeleton for the wire rope non-destructive testing relying on the theoretical analysis and 3D transient magnetic field simulation. The experiment results proved that the signal to noise ratio of coil with the iron core proposed in this paper increased almost six times, making it easier for defect analysis.…”

Section: Detection Sensormentioning

confidence: 99%

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Zhou

Zhu

et al. 2019

Applied Sciences

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As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

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Section: Detection Sensormentioning

confidence: 99%

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Zhou

Zhu

et al. 2019

Applied Sciences

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“…Common magnetoelectric conversion devices include induction coils, fluxgates, Hall effect sensors, and magnetoresistive sensors. In order to improve the signalto-noise ratio (SNR) of data collected from the nondestructive testing (NDT) induction coil sensor and along with the convenience of installation, Yan et al [9] improved the structure of the traditional coil sensor, adding a wedge-shaped iron core and removing the arcs on both sides of the iron core to simplify the winding. Kim and Park [10] designed a fourchannel Hall sensor array for local defect MFL testing and subsequently improved it to make an eight-channel Hall sensor array for collecting two-dimensional (2D) MFL information [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Variational Mode Decomposition and k‐Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

Zheng

Zhang

2019

Shock and Vibration

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In this paper, we present a nondestructive testing device for wire rope by unsaturated magnetic excitation as an alternative to existing magnetic flux leakage (MFL) detection devices. The existing devices are heavy and inconvenient and offer somewhat lower accuracy and low signal-to-noise ratios (SNRs). Our design implements variational mode decomposition (VMD) and a wavelet transformation to remove noise from the raw MFL signals. Grayscale images representing the denoised MFL data simplify visual interpretation of the results and location of defects in both axial and circumferential directions. Quantification of defects is enabled using a k-nearest neighbor (KNN) algorithm to classify broken wires. Experimental results show that our design offers lighter weight, better convenience, and high sensitivity along with better removal of noise and more accurate classification of defects.

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“…Pan et al [7] proposed a simple and portable magnetic detector device, which was very light and easy to design and manufacture. e leakage magnetic field on the surface of the wire rope is usually collected by magnetic sensors, which includes Hall sensor [8], coil sensor [9], fluxgate sensor [10], giant magnetoresistance (GMR) sensor [11], tunnel magnetoresistance (TMR) sensor [12]. e circumferential resolution and sensitivity of magnetic sensors array are important for the detection of wire rope damage.…”

Section: Introductionmentioning

confidence: 99%

“…And the sensitivity of the magnetic sensor also affects the effectiveness of detecting damages. Yan et al [9] designed a kind of iron core as coil winding skeleton, and their design improved the signal-to-noise ratio (SNR) of magnetic flux leakage signal and simplified coil sensor. Lei et al [11] proposed a detection device based on high-sensitivity GMR sensors, which solved the problem of low SNR caused by the small diameter and large lift off.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Nondestructive Testing of Wire Rope Using Image Super‐Resolution Method and AdaBoost Classifier

Zhang

2019

Shock and Vibration

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Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing (NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to solve the problems of large volume, complex operations, and limited circumferential resolution due to sensor size in traditional devices. e device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of the magnetized wire rope. ese sensors on the double detection board are staggered and evenly arranged on the circumference of the wire rope. A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combining principal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve the resolution and quality of defect images. Image quality measurement and comparison experiments are designed to verify that defect images are effectively enhanced. An AdaBoost classifier is designed to classify defects by texture features and invariant moments of defect images. e experimental results show that the detection device not only improves the circumferential resolution, but also the operation is simple; the resolution and quality of the defect images are improved by the proposed super-resolution algorithm, and defects are identified by using the AdaBoost classifier.

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Improve the signal to noise ratio and installation convenience of the inductive coil for wire rope nondestructive testing

Cited by 42 publications

References 13 publications

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Application of Variational Mode Decomposition and k‐Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

Quantitative Nondestructive Testing of Wire Rope Using Image Super‐Resolution Method and AdaBoost Classifier

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